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Sensors calibration plays a crucial role in controlling systems and achieving fault-tolerant control by ensuring accuracy, performance, safety, energy efficiency, and compliance with standards. It is an essential to maintain the reliability and effectiveness of modern control systems across various applications. In this paper, we represent a new algorithm that processes a set of raw data collected by a sensor to find the mapping function that relates the raw data to the real value of the measured signal by the sensor. Working on sensors with an unknown mapping function, unknown parameters, or with external disturbances, that affects their behaviour, represents a problem; moreover, it takes a lot of time and effort to calibrate the sensor before each use. Several techniques were used to overcome these aspects mostly by recording the output of the sensor for different input values that change manually, to calibrate the sensor. However, the represented technique in this paper can easily provide us with the input/output model of a specific sensor by doing only one experiment; it also improves the accuracy of the measurements as it is a self-calibrating technique that reduces the nonlinearity and noise problems to deliver a better estimation of the measured signal, which is validated in this paper experimentally using a low-cost current sensor by comparing the obtained results from this algorithm with the results using the extracted input/output model illustrated in the datasheet. Furthermore, if the sensor is pretty poor, and if the application requires more precision, the provided estimation by the mapping function can be mixed with other sensor/s readings using sensor fusion algorithms to find a more precise value of the input. The represented algorithm can also perform self-calibration while evaluating the functionality of the application and the variations of the temperature and other external disturbances that affect the sensor.
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Wydawca
Czasopismo
Rocznik
Tom
Strony
446--462
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
- Electrical Engineering Department, University Yahia Fares, Medea, Algeria
autor
- Automatic Department, National Polytechnic School, Algiers, Algeria
autor
- Electrical Engineering Department, University Yahia Fares, Medea, Algeria
autor
- Electrical Engineering Department, University of Haute Alsace, Mulhouse, France
Bibliografia
- Anderson, E., Bai, Z. and Dongarra, J. (1992). Generalized QR Factorization and Its Applications. Linear Algebra and Its Applications, 162, pp. 243–271. doi: 10.1016/0024-3795(92)90379-O.
- Badura, M., Batog, P., Drzeniecka-Osiadacz, A. and Modzel, P. (2019). Regression Methods in the Calibration of Low-Cost Sensors for Ambient Particulate Matter Measurements. SN Applied Sciences, 1, p. 622.
- Cordero, J. M., Borge, R. and Narros, A. (2018) Using Statistical Methods to Carry Out in Field Calibrations of Low Cost Air Quality Sensors. Sensors and Actuators B: Chemical, 267, pp. 245–254. doi: 10.1016/j.snb.2018.04.021.
- Demeure, C. J. and Scharf, L. L. (May, 1989). Fast least squares solution of vandermonde systems of equations. In: International Conference on Acoustics, Speech, and Signal Processing, IEEE. pp. 2198–2210. 460
- Djokic, B. and So, E., (2005). Calibration system for electronic instrument transformers with digital output. In: IEEE Transactions on instrumentation and Measurement, 54(2), pp. 479–482.
- Elmenreich, W., (2002). Sensor fusion in time-triggered systems (Doctoral dissertation, Technische). Universität Wien.
- Gao, P. (2018). Power System Current Measurement using Sensor Array Techniques. University of Alberta doctorat thesis.
- Hong, S., Luo, M. and Wang, X. (2023). Control of BLDC Motor Drive with Single Hall Sensor Considering Angle Compensation. Power Electronics and Drives, 8(1), pp. 299–309.
- Hu, Z., Chen, D., Kallel, A. Y., Wang, S. and Kanoun, O. (2022). Self-Calibrated AC Zero Potential Circuit for Two-Dimensional Impedimetric Sensor Matrices. IEEE Sensors Journal, 22(6), pp. 6002–6009. doi: 10.1109/JSEN.2022.3147038.
- Hwang, J. Y., Park, J. H., Choi, J. H., Uhm, J. I., Lee, G. H. and Lim, H. S. (2021). A Precise Current Detection Method using a Single Shunt and FET Rds (on) of a Low-Voltage Three-Phase Inverter. Electronics, 11(1), p. 9. doi: 10.3390/electronics11010009.
- Khan, S. A., Shahani, D. T. and Agarwala, A. K. (2003). Sensor Calibration and Compensation using Artificial Neural Network. ISA Transactions, 42(3), pp. 337–352.
- Kurniawan, I. H., Hayat, L. and Pratama, D. K. (2022). IoT Based Electrical Energy Monitoring System. In: AIP Conference Proceedings, AIP Publishing, Purwokerto, Indonesia, 2578, p. 040005.
- Lazarević, Đ., Živković, M., Kocić, Đ. and Ćirić, J. (2022). The Utilizing Hall Effect-Based Current Sensor ACS712 for True RMS Current Measurement in Power Electronic Systems. Scientific Technical Review, 72(1), pp. 27–32.
- Leon, S. J., Björck, Å. and Gander, W. (2013). GramSchmidt Orthogonalization: 100 Years and More. Numerical Linear Algebra with Applications, 20(3), pp. 492–532.
- Lifton, J. J. and Liu, T. (2020). Evaluation of the Standard Measurement Uncertainty due to the ISO50 Surface Determination Method for Dimensional Computed Tomography. Precision Engineering, 61, pp. 82–92.
- Pertijs, M., (2014). Calibration and Self-Calibration of Smart Sensors. Smart Sensor Systems. In: Emerging Technologies and Applications, pp.17–41.
- Shalamov, S. P. (2016). An induction sensor for measuring currents of nanosecond range. Электротехника и электромеханика, 5 (eng), pp. 57–60.
- Teler, K. and Orłowska-Kowalska, T. (2023). Analysis of the Stator Current Prediction Capabilities in Induction Motor Drive using the LSTM Network. Power Electronics and Drives, 8(1), pp. 31–52. doi: 10.2478/pead-2023-0003.
- Wu, G., Zhang, M. and Guo, F. (2020). Self-Calibration Direct Position Determination using a Single Moving Array with Sensor Gain and Phase Errors. Signal Processing, 173, p. 107587. doi: 10.1016/j.sigpro.2020.107587.
- Xu, L., Xu, W., Cao, Z., Liu, X. and Hu, J., (2014). Multiple parameters׳ estimation in horizontal well logging using a conductance-probe array. Flow Measurement and Instrumentation, 40, pp.192–198.
- Yeong, D. J., Velasco-Hernandez, G., Barry, J. and Walsh, J. (2021). Sensor and Sensor Fusion Technology in Autonomous Vehicles: A review. Sensors, 21(6), p. 2140. doi: 10.3390/s21062140.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a735a657-d1af-4129-b750-94827813558a
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